INTELLIGENT NETWORK SELECTION USING FUZZY LOGIC FOR 4G WIRELESS NETWORKS

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN

0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME

451

INTELLIGENT NETWORK SELECTION USING FUZZY LOGIC FOR

4G WIRELESS NETWORKS

C.Amali [1]

,Bibin Mathew[2]

, B.Ramachandran[3]

[1][2][3]

Department of Electronics and Communication Engineering

S.R.M University, Chennai, India-603203

ABSTRACT

Due to the heterogeneity and the diversity of access networks, various user

applications with different Quality of Service (QoS) requirements pose new challenges on

multi-interface Mobile Terminal (MT) in designing optimal network selection algorithm for

guaranteeing seamless QoS support to the users. Thus, service adaptive QoS metrics of

mobile users can be improved by sharing the resources of different Radio Access Networks

(RAN) efficiently. A new intelligent Vertical Handoff (VHO) scheme is presented that

utilizes Fuzzy Logic based Linguistic Variables to estimate the necessity of handoff and to

determine a new point of attachment in order to fulfill the end users requirements. As each

traffic has a different set of QoS requirements, separate Fuzzy Logic Controllers (FLC) are

used for each traffic to improve the overall performance of proposed system. To maintain

continuous services while moving in heterogeneous environments, a Fuzzy Multi Attribute

Decision Making (MADM) access network selection function is used to select a suitable

network. In order to achieve the service continuity, a vertical handoff decision scheme is

proposed to enhance the service mobility by selecting the suitable network based on QoS

requirements of applications and network characteristics.

Keywords— Fuzzy logic, Handoff Decision, Heterogeneous Networks, NEF, QoS

1. INTRODUCTION

The next generation wireless communication systems are expected to integrate

multiple Radio Access Technologies (RAT) in terms of services and application

requirements. Thus, a user will get access to a range of services having different bandwidths

and QoS requirements with the multimode terminal in "Always Best Connected" (ABC) [1]

manner.

INTERNATIONAL JOURNAL OF ELECTRONICS AND

COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)

ISSN 0976 – 6472(Online)

Volume 4, Issue 2, March – April, 2013, pp. 451-461 © IAEME: www.iaeme.com/ijecet.asp

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452

The strengths of third generation (3G) cellular networks such as UMTS and CDMA

2000 consist of their global coverage where as their weakness lie in bandwidth capacity and

operation costs. With the fast evolution of wireless technologies, a number of wireless access

networks are now emerging thereby creating a heterogeneous network environment for both

mobile users and internet service providers (ISPs). However, any single type of these wireless

networks cannot provide all types of services, e.g., wide coverage and high bandwidth. On

the other hand wireless LAN (WLAN) offers higher bandwidth with low service costs, but it

provides small coverage area [2]. IEEE802.16x or WiMAX systems are optimized to provide

real time high data rate services in WMAN environments. There is no single system that is

good enough to replace all the other technologies.

The strength of 4G system relays on integrating the existing and newly developed

wireless systems instead of putting efforts into developing new radio interfaces and

technologies to provide seamless mobility and better service quality for mobile users. In this

paper, the coexistence of UMTS, WLAN and WiMAX access networks are considered as a

heterogeneous wireless network. When connections have to switch between heterogeneous

networks for performance and high availability reasons, seamless vertical handoff is

necessary to provide uninterrupted services to the mobile users.

VHO is the seamless transfer of an ongoing user session between different

heterogeneous radio access technologies. The vertical handover decision process determines

when and where to hand over in a heterogeneous environment when the user is on the move.

Decision criteria include MT speed, user preferences, network conditions and application

requirements. For each network, there is a Received Signal Strength (RSS) threshold value

below which connection break with active base station. Therefore, the signal strength must be

greater than threshold point to maintain the connection with serving network. The signal

becomes weak as mobile moves far away from serving base station and gets stronger signal

towards new base station as it moves closer. There is a need for Handoff if RSS of active

base station decreases below threshold level to maintain the connection.

The aim of this work is to design and simulate an application specific VHO among

WiMAX, UMTS and WLAN using fuzzy tool. The applications taken into the consideration

are conversational and real time video streaming and each of these applications require a

different QoS. All the three technologies do not support these applications with equal QoS.

Each wireless technology has a limit on mobility support and cost of service offered. Users

will always look for lower cost, keeping QoS intact; hence a VHO algorithm must try to

select the most cost effective network as target network. WLAN has the least cost of service

followed by WiMAX and UMTS. In this work, speed of the vehicle (mobility) is considered

as an important factor to evaluate the networks before ranking the networks based on the

application requirements.

The remainder of this paper is organized as follows: Related and existing works are

discussed in section 2. Overview of proposed algorithm is presented in section 3. Section 4

comprises various modules of proposed algorithm. Performance assessment is carried out in

section 5 and finally conclusion is given in section 6.

2. RELATED WORK

To improve the performance of the handoff scheme, a network discovery algorithm

based on the fuzzy logic multiple objective decision making system is presented in [3]. In [4]

& [5], an adaptive fuzzy based handoff decision system is developed which consider

parameters such as data rate, cost and RSSI to obtain training elements. With the training



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element of ANFIS from fuzzy based system, the rules and the membership function can be

properly tuned to optimize the handoff performance. In [6], a Modified Weight Function

based Network Selection Algorithm (MWF-NSA) that considers user preference and

application profile is proposed in deciding the weight functions of the networks. In [7] fuzzy

multiple attribute decision is used for vertical handoff decision. The application requirements

for both conversational and streaming are given in [8].

Although, there have been various vertical handoff algorithms available in the

literature, our proposed work aims to incorporate the user speed, characteristics of networks,

user preference, cost and QoS requirements of different traffic in fuzzy logic based network

selection algorithm to select the optimal network to provide efficient vertical handoff for

heterogeneous wireless networks. In the proposed algorithm, the network related, terminal

related, user related and service related attributes are included, which improves the accuracy

of the VHO.

3. OVERVIEW OF PROPOSED ALGORITHM

Due to the heterogeneity and the diversity of access networks, various user

applications with different QoS requirements pose new challenges in designing optimal

network selection algorithm for guaranteeing seamless QoS support to the users. Thus, VHO

is necessary to provide uninterrupted services to mobile users anywhere and anytime in 4G

Networks. The various techniques used for executing handoff can be classified into Mobile-

Controlled Handoff (MCHO), Network-Controlled Handoff (NCHO) and Mobile-Assisted

Handoff (MAHO). In MCHO, the mobile node continuously monitors the signal strength of

access points and initiates the handoff procedure when certain handoff decision is triggered.

Fuzzy logic based VHO algorithms are best suitable for MCHO in integrated networks. The

fuzzy logic controllers are designed for the different modules of proposed algorithm using

fuzzy rules base. The fuzzification comprises the process of transforming the crisp inputs into

the fuzzy sets via the membership functions. The fuzzy rules base consists of a collection of

fuzzy if-then rules to represent the human knowledge about the problem.

In fuzzification process, if inputs X={x1, x2, …, xm} are memberships of fuzzy set

Y={ A~

, B~

,…, M~

}, respectively, then the degree of membership of X={ x1, x2, …, xm} is

given by

[0,1] )X(Y where ),,( ∈µµµµX

H

X

M

X

L

The reason for using Fuzzy MADM is that

i) multiple parameters can be processed simultaneously and provides the best solution

for VHO decision when the input exhibits uncertainty.

ii) the traditional fuzzy based Vertical Handoff Decision Algorithm (VHDA) needs

defuzzification, which may increase the handoff delay. But the proposed VHDA selects the

best suitable network based on the values of network evaluation function.

International Journal of Electronics and

0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March

Fig.1: Functional Block Diagram of Proposed Mechanism

Fuzzy logic based proposed algorithm is divided into four modules.

block diagram of proposed mechanism is shown in fig.

whether handover is necessary when the mobile terminal is moving across heterogeneous

wireless networks. The next mod

handoff threshold value. In the second module, User Satisfaction D

based on the speed, network load and cost of service in order to select the netwo

supports. In the third module,

streaming traffic are considered to determine

uninterrupted services to mobile users. In fourth module, the perfo

evaluated by calculating Network Evaluation F

and QoS factor of ongoing service.

common to both urban and suburban environments where

The simulation is based on locations where all the three networks are available.

coverage area is larger than UMTS and WLAN

Fig.2: Simulation Scenario for Urban and Suburban Environments

4. FORMULATION OF PROPOSED ALGORITHM

The proposed FUZZY based VHO

in fig.3. In this algorithm, MT speed, service cost, application profile and netw

characteristics are considered for the evaluation of available networks. The detailed

explanation of the modules is described in the following sub sections.


6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME

454

: Functional Block Diagram of Proposed Mechanism

based proposed algorithm is divided into four modules. The functional

sed mechanism is shown in fig.1. The first module determines


ess networks. The next module is executed only if handoff factor is high

. In the second module, User Satisfaction Degree (USD)

network load and cost of service in order to select the netwo

supports. In the third module, QoS requirements of conversational and real time video

streaming traffic are considered to determine QoS factor for the available networks to provide

uninterrupted services to mobile users. In fourth module, the performance of networ

evaluated by calculating Network Evaluation Function (NEF) based on the va

ongoing service. The simulation scenario is shown in fig.2. The scenario is

common to both urban and suburban environments where all the networks will be present.

locations where all the three networks are available.

ea is larger than UMTS and WLAN for simulation in the proposed scheme

: Simulation Scenario for Urban and Suburban Environments

FORMULATION OF PROPOSED ALGORITHM

The proposed FUZZY based VHO algorithm is formulated using 4 modules as shown

In this algorithm, MT speed, service cost, application profile and netw


explanation of the modules is described in the following sub sections.

Communication Engineering & Technology (IJECET), ISSN

April (2013), © IAEME

The functional

The first module determines


factor is higher than the

(USD) is evaluated

network load and cost of service in order to select the network that

requirements of conversational and real time video

factor for the available networks to provide

rmance of networks is

unction (NEF) based on the values of USD

2. The scenario is

all the networks will be present.

locations where all the three networks are available. WiMAX

for simulation in the proposed scheme.

: Simulation Scenario for Urban and Suburban Environments

modules as shown

In this algorithm, MT speed, service cost, application profile and network




455

Fig.3: Flow Diagram of Proposed Algorithm

4.1 HANDOFF FACTOR ESTIMATION This module examines the conditions of serving network and estimates the handoff

factor. By estimating the handoff factor to initiate the handoff at the right time, end user

satisfaction can be maximized which in turn improves the overall QoS in heterogeneous

wireless networks. RSS of serving network is estimated using suitable path loss models based

on the environments in which mobile terminal is moving. If RSS of serving network keep on

decreasing, there is a need for handoff to another network to provide uninterrupted services to

mobile users.

For this case, FLC is deployed using fuzzy logic toolbox in MATLAB. The Fuzzy Inference

System (FIS) accepts the fuzzified values and interprets the fuzzified values to the outputs

based on the user defined rules. Whenever the RSS drops below the threshold value, the

probability of handoff becomes high. Thus, handoff factor is estimated to select the best

suitable network. The next stage of network evaluation process is evaluated only when

handoff factor is high. The corresponding FIS model for handoff Factor estimation is shown

in fig.4. Mamdani system is used here because of its wide spread acceptance and it is well

suited to human input.

Fig.4: RSS Handoff Decision controller

4.2 USER SATISFACTION DEGREE (USD) CALCULATION

Our proposed scheme chooses only a few parameters that are critical to maximize the

end users satisfaction while performing VHO in heterogeneous environment. In this stage, the

performance of networks are evaluated based on the parameters like MT speed, service cost

and network load. The QoS requirements of ongoing service can be provided easily by

maximizing end user satisfaction based on their preference, location and application

requirements. Thus available networks are evaluated to select the network capable of

satisfying the user request during VHO operation. For example, networks with less coverage

area cannot support the users with high speed. It requires large number of handoff to

complete the ongoing service in MT.



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Before evaluating the networks based on given application, it is necessary to evaluate

the networks based on speed, user preference (cost) and network conditions (load) to reduce

the outage probability and also to avoid unnecessary handovers in heterogeneous wireless

networks. Thus, the end users can specify their needs and preferences by assigning

membership functions to each system parameter. FLC is designed by assigning membership

functions to speed, cost and network load to evaluate the networks according to their

characteristics. FIS accepts the input fuzzy sets and maps with outputs based on the fuzzy

rules. For example, if the MT speed is low and user preference is low cost, then WLAN will

be selected. The corresponding membership functions are given in fig.5

Fig.5: Membership Functions of Speed, Load and Cost

4.3 QoS FACTOR EVALUATION

After estimating the user satisfaction degree, the characteristics of different traffic

must be taken into account in designing optimal network selection algorithm for guaranteeing

seamless QoS support to the users. In this module, two fuzzy logic controllers are deployed

for conversational and video streaming traffic. Service sensitivity parameters such as RSS,

data rate and connection delay are considered to achieve guaranteed QoS to mobile users.

The membership functions are assigned as low, medium and high to the input variables and

fuzzy rules are made as per the requirements of 3GPP QoS classes.

For conversational traffic, RSS is an important factor to maintain a good link between

MT and BS with low delay and minimum bandwidth requirements. But, real time video

streaming application requires large bandwidth and RSS with tolerable delay. The application

requirements considered for the simulation is shown in Table1.

Table 1: Conversational and Streaming Application Requirements

Application Symmetry Data rate Delay RSS

Conversational

Two Way

4-64 kbit/s

<150 ms

<400 ms

(max limit)

High

Streaming

One Way

16-384

kbit/s

< 10 s

High



4.4 NETWORK EVALUATION FUNCTION

After filtering the available networks using

NEF is calculated. NEF is determined to provide optimization between user satisfaction

degree and QoS factor. The outputs of the previous modules are combined in parallel

include the mobility, QoS requirements of applications and network characteristics in order to

provide best solution to the network selection problem.

By selecting the network with maximum

number of handoffs required for

guaranteed QoS. Thus, the MT is always

continuity and also to guarantee QoS

5. PERFORMANCE ASSESSMENT

The performance of the VHDA

scenario as shown in fig.2. The NEF is calculated using SIMULINK model of MATLAB.

The user satisfaction degree is plotted with respect to velocity for different percentage of

traffic load and cost of service. The correspon

shows at low percentage of cost and traffic load,

Km/hr thereafter WiMAX USD

WiMAX are having same value

it supports higher velocity. For medium cost and lo

better which is shown in fig.6.3. The cost of servic

traffic load, that’s why UMTS USD is higher in fig.

Fig.6: User Satisfaction Degree of WLAN, UMTS and WiMAX

10% Traffic Load.(6.2) for 10% Cost and 50% Traffic Load. (6.3) for 50% Cost and

10% Traffic Load. (6.4) for 70% Cost and 70% Traffic Load



457

NETWORK EVALUATION FUNCTION ESTIMATION

After filtering the available networks using user satisfaction degree and QoS

NEF is determined to provide optimization between user satisfaction

factor. The outputs of the previous modules are combined in parallel


provide best solution to the network selection problem.

By selecting the network with maximum USD and QoS factor as target ne

quired for MT to complete its connection can be reduced with

, the MT is always connected to the best suitable network for service

QoS requirements of ongoing service.

PERFORMANCE ASSESSMENT

VHDA is tested within the framework of chosen simulation

The NEF is calculated using SIMULINK model of MATLAB.


. The corresponding figures are shown in fig.6

ntage of cost and traffic load, WLAN is the preferred network up to 20

value is increasing. In fig.6.2 after 20km/hr both UMTS

up to 50km/hr. After that WiMAX USD is increasing since

. For medium cost and low traffic load WiMAX USD

6.3. The cost of service is high in UMTS and it supports high

USD is higher in fig.6.4.

n Degree of WLAN, UMTS and WiMAX (6.1) for 10% Cost and

for 10% Cost and 50% Traffic Load. (6.3) for 50% Cost and

10% Traffic Load. (6.4) for 70% Cost and 70% Traffic Load



user satisfaction degree and QoS factor,

NEF is determined to provide optimization between user satisfaction

factor. The outputs of the previous modules are combined in parallel to


and QoS factor as target network, the

its connection can be reduced with

connected to the best suitable network for service

chosen simulation

The NEF is calculated using SIMULINK model of MATLAB.


6. The fig.6.1

preferred network up to 20

after 20km/hr both UMTS and

increasing since

w traffic load WiMAX USD is performing

e is high in UMTS and it supports high

10% Cost and

for 10% Cost and 50% Traffic Load. (6.3) for 50% Cost and



The SIMULINK model of the propo

according to the functional block diagram given in

Fig.7: SIMULINK model of the proposed mechanism



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The SIMULINK model of the proposed algorithm is shown in fig.7. The model works

l block diagram given in Fig.1.

: SIMULINK model of the proposed mechanism



7. The model works



The NEF table is shown in table 2. As an example

each USD case, four different QoS cases

(a) 15 % speed and 10% cost of service and traffic load. (b) 30% speed, 50% traffic load and

10% cost of service. (c) 50% speed and cost of service and 10% traffic load. (d) 70% of

speed, cost of service and traffic load. For all the above examples

parameters are considered. QoS

by high, medium and low in table 2. This

NEF value calculated from USD and QoS values. Delay is considered to be an important

factor in conversational application whereas data rate is more important in

which means bandwidth requirement is more

WiMAX is having higher bandwidth when compared to UMTS. UMTS and WiMAX is

having higher coverage area and supports more load than WLAN.

diagrams of the examples shown in table

that for low value of speed, traffic load and cost, WLAN is

conversational and video streaming applicat

a corresponding change in the final NEF. For medium values of

is the preferred network for conversational and WiMAX for

the cost of service increases, UMTS

WiMAX. If QoS parameters like RSS and data rate have a

then all the networks have a low NEF for all the cases of USD.

selected for incoming traffic.

Table 2: Example of NEF Values for Conversati



459

The NEF table is shown in table 2. As an example, four cases of USD are taken and in

four different QoS cases are considered. The examples considered here a



speed, cost of service and traffic load. For all the above examples, four cases of QoS

considered. QoS parameters are RSS, data rate and delay and are

, medium and low in table 2. This table gives the final selection of network based on


factor in conversational application whereas data rate is more important in video s

uirement is more in video streaming applications. WLAN and


having higher coverage area and supports more load than WLAN. The corresponding bar

diagrams of the examples shown in table 2 are given in fig.8. From the fig.8, it is understood

traffic load and cost, WLAN is the preferred network for both

streaming applications. As the USD & QoS values changes, there is

a corresponding change in the final NEF. For medium values of speed, load and cost, UMTS

the preferred network for conversational and WiMAX for video streaming applications

, UMTS is selected because of its higher cost than WLAN and

rs like RSS and data rate have a low value and when delay is high

low NEF for all the cases of USD. In this case, no

: Example of NEF Values for Conversational and Streaming Applicatio



four cases of USD are taken and in

considered. The examples considered here are



ur cases of QoS

, data rate and delay and are represented

table gives the final selection of network based on


video streaming,

streaming applications. WLAN and


The corresponding bar

it is understood

the preferred network for both

changes, there is

eed, load and cost, UMTS

streaming applications. As

than WLAN and

when delay is high,

no networks are

onal and Streaming Applications



Fig.8: Graph showing NEF values (

conversational and streaming applications

service and traffic load. (8.2)

service. (8.3)NEF for 50% speed, 10%

70% speed and 70% traffic load and 70% cost of service



460

: Graph showing NEF values (from Table 2) of different networks for

conversational and streaming applications. (8.1) NEF for 15% speed and 10% of cost

.2)NEF for 30% speed, 50% traffic load and10% cost

.3)NEF for 50% speed, 10% traffic load and 50% cost of service.

70% speed and 70% traffic load and 70% cost of service



Table 2) of different networks for

.1) NEF for 15% speed and 10% of cost of

load and10% cost of

. (8.4)NEF for



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6. CONCLUSION

This paper proposes an intelligent fuzzy logic based network selection for 4G wireless

networks. The developed algorithm considers the USD as well as QoS parameters for both the

conversational and video streaming applications.The USD, QoS and NEF are evaluated using

separate fuzzy logic controllers and tested using SIMULINK .Whenever there is a need for

handoff, the NEF is calculated using USD and QoS of parameters from the available list of

networks present at that time. The network with high NEF is selected as the preferred network

and is going to serve the user. In the proposed algorithm, the network related, terminal related,

user related and service related attributes are included to minimize the number of VHO. Thus, the

best suitable network is selected for the ongoing traffic by providing optimization between the

complexity and improved QoS. The proposed scheme can benefits both users and networks by

handling the uncertainity and time varying information using fuzzy logic rules.

For further research, fuzzy logic technique can be integrated with other MADM methods

to provide more efficient network selection and also to analyze the effect of complexity in the

proposed scheme.

REFERENCES

[1] E.Gustaffson and A.Jonsson, “Always Best Connected”, IEEE wireless Communication ,

vol.10, no.1, Feb.2003, pp.49-55.

[2] Q. Song and A. Jamilipour , “Network Selection in an Integrated Wireless LAN and UMTS

Environment Using Mathematical Modeling and Computing Techniques”, IEEE Wireless

Communications, June 2005; DOI: 10.1109/MWC.2005.1452853.

[3] M. Lahby, C. Leghris. and A. Adib. “A Hybrid Approach for Network Selection in

Heterogeneous Multi-Access Environments”,In the Proceedings of the 4th IFIP International

Conference on New Technologies, Mobility and Security (NTMS), pp.1-5, Paris France, Feb

2011.

[4] Xu Haibo, Tian Hui, Zhang Ping, “A novel terminal-controlled handover scheme in

heterogeneous wireless networks”, Computers and Electrical Engineering 36 (2010) pp 269–279.

[5] Ali Çalhan & Celal Çeken, “An Adaptive Neuro-Fuzzy Based Vertical Handoff Decision

Algorithm for Wireless Heterogeneous Networks” proceedings of 21st Annual IEEE International

symposium on Personal, Indoor and Mobile Radio Communications, 2010, pp 2271-2276.

[6] C.Amali and B.Ramachandran, “Modified Weight Function Based Network Selection

Algorithm for 4G Wireless Networks”, ACM International Conference on Advances in

Computing, Communication and Informatics(ICACCI 2012) Chennai, India, pp. 292-299, 3rd –

5th August 2012.

[7] L. Xia, LG. Jiang, and C. He, "A Novel Fuzzy Logic Vertical Handoff Algorithm with Aid of

Differential Prediction and Pre-Decision Method", Proc. of IEEE ICC'06, 2006, pp. 5665-5670.

[8] Carlos Ramirez-Perez and Victor M. Ramos R. “A QoS hierarchical decision scheme for

vertical handoff”, 2012 8th International Caribbean Conference on Devices, Circuts and Systems

(ICCDCS).

[9] Prof. J. R. Pathan, Prof. A. R. Teke, Prof. M. A. Parjane and Prof. P.S. Togrikar, “Dropping

Based Contention Resolution for Service Differentiation to Provide Qos in Wdm Obs Networks”,

International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013,

pp. 218 - 228, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

[10] Jayashree Agrakhed, G. S. Biradar and V. D. Mytri, “Optimal Qos Routing With Prioritized

Region Scheduling Over Wmsn”, International Journal of Computer Engineering & Technology

(IJCET), Volume 3, Issue 1, 2013, pp. 289 - 304, ISSN Print: 0976 – 6367, ISSN Online: 0976 –

6375.

INTELLIGENT NETWORK SELECTION USING FUZZY LOGIC FOR 4G WIRELESS NETWORKS

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